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ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets

Hong-Dong Li, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, Jianxin Wang
doi: https://doi.org/10.1101/255711
Hong-Dong Li
1Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, P.R. China
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Yunpei Xu
1Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, P.R. China
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Xiaoshu Zhu
1Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, P.R. China
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Quan Liu
1Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, P.R. China
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Gilbert S. Omenn
2Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, United States
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Jianxin Wang
1Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, P.R. China
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ABSTRACT

Motivation Clustering analysis is essential for understanding complex biological data. In widely used methods such as hierarchical clustering (HC) and consensus clustering (CC), expression profiles of all genes are often used to assess similarity between samples for clustering. These methods output sample clusters, but are not able to provide information about which gene sets (functions) contribute most to the clustering. So interpretability of their results is limited. We hypothesized that integrating prior knowledge of annotated biological processes would not only achieve satisfying clustering performance but also, more importantly, enable potential biological interpretation of clusters.

Results Here we report ClusterMine, a novel approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets, e.g., in Gene Ontology. In addition to outputting cluster membership of each sample as conventional approaches do, it outputs gene sets that are most likely to contribute to the clustering, a feature facilitating biological interpretation. Using three cancer datasets, two single cell RNA-sequencing based cell differentiation datasets, one cell cycle dataset and two datasets of cells of different tissue origins, we found that ClusterMine achieved similar or better clustering performance and that top-scored gene sets prioritized by ClusterMine are biologically relevant.

Implementation and availability ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php

Contact jxwang@csu.edu.cn

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 29, 2018.
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ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets
Hong-Dong Li, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, Jianxin Wang
bioRxiv 255711; doi: https://doi.org/10.1101/255711
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ClusterMine: a Knowledge-integrated Clustering Approach based on Expression Profiles of Gene Sets
Hong-Dong Li, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, Jianxin Wang
bioRxiv 255711; doi: https://doi.org/10.1101/255711

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